Individual system performance management

ABSTRACT

Individual System Performance Management apparatus employs in one manifestation Sequential Empirical Optimization (SEO) in its illustrative version to manage a sequence of periodic readjustment to adjust inputs of an input/output system with the aim to maximize the cumulative sequence of overall value delivered by the system. SEO continually updates its learning from the stored run data. In this discussion, the term “adjusting” control inputs is used the same as “setting” control inputs, including adjusting or setting things by hand.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application hereby claims the benefit of the provisionalpatent application of the same title, Ser. No. 60/755,534, filed on 30Dec. 2005, the disclosure of which is hereby incorporated by referencein its entirety.

FIELD OF THE INVENTION

The present invention relates, in general, to devices to optimizeoperating performance of existing systems, by managing better anddynamically the adjustable decision inputs, and in one embodimentlearning and refining how to do so by the continual collection ofoperating data.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention,and, together with the general description of the invention given above,and the detailed description of the embodiments given below, serve toexplain the principles of the present invention.

FIG. 1 is a block diagram of an Individual System Performance Managementapparatus.

FIG. 2 is a diagram depicting an Effective Operating Area to satisfy anoptimization requirement for the area of acceptable output means.

FIG. 3 is a plot of results from the Individual System PerformanceManagement apparatus.

FIG. 4 is another plot of results from the Individual System PerformanceManagement apparatus.

DETAILED DESCRIPTION OF THE INVENTION

The Individual System Performance Management (ISPM) advises decisions tomanage the operations of an individual input/output (I/O) system toimprove its operating performance. Depending on the uses andcircumstances the decisions may be implemented automatically. ISPM is adevice with multi-input, multi-output logic that uses data andmathematics to learn and aid the user in how to achieve better overallperformance with an existing system.

Desirability of performance for each individual I/O system may bedefined by the user or expert by: (a) any input/output value satisfyinghigh and/or low constraints (typically capacity and safetyconsiderations), and/or (b) when the constraints are satisfied, by thevalue of a Performance Index to be maximized or minimized. ThePerformance Index may be calculated as a function of inputs and outputvalues—and coefficients (e.g., fuel unit cost) may be updated by theuser. The Performance Index may be an economic evaluation or any measureof operating “performance”, “goodness”, “satisfaction” or“desirability”.

Operations of an individual I/O system creates output (response) valueswhich depend on the values of the inputs. The ISPM is useful when thereare various possible levels of operating performance or itsdesirability—which means that there are sufficiently large possiblecombinations of input values.

The inputs range from the inevitable (e.g., outside air temperature,purity of an ingredient), through those which are difficult to choose oradjust (e.g., gear ratios), to those inputs that are easy to adjust(e.g., flow settings).

Even though there is not a distinct dichotomy, and in order to be ableto use current mathematical tools, it is useful at any one time to thinkabout two types of inputs (see FIG. 1 below):

(1) Conditions—that determine the corresponding optimal Device Decisionvalues;

-   -   (a) the Inevitable inputs which are practically impossible to        change;    -   (b) the decisions which are already set in place or the user is        about to implement; and    -   (c) for system with memory, recent past inputs and outputs (done        automatically).

(2) Device Decisions for which the user wants the ISPM to suggestnear-optimal values. This includes modifications to the advice given bybuilt-in control rules to get better operating performance. Whichdecisions belong to the set of Device Decisions is determined at thetime of requesting ISPM for an advice.

One objective of the ISPM is to enable making a sequence of DeviceDecisions that would make operations the most desirable possible, giventhe possibly changing known Conditions. That is, an objective of ISPM isoptimization—sometimes referred to as “conditional optimization”.

One application is when a very accurate mathematical function of thesystem's performance do not exist. Another application is when thesystem is not a production process. Yet another application is when thesystem is an individual living being. Yet another application is whenthe living being is a human body. Yet another application is when theliving being is the user's personal body. Yet another application iswhen the human body has a disease. Yet another application is when thedisease is chronic. Yet another application is when the disease isdiabetes.

As applied to the management of diabetes this product may be calledPersonal Diabetes Management System (PDMS). One possible example of aPDMS application at any one time would be:

(1) Conditions

-   -   (a) Inevitable situations that the body has to live with, such        as temperature, forced activities, forced ingestions, medicament        availability, etc.;    -   (b) Decisions that the user wishes to make for whatever reasons,        such as going for a walk, eating a large steak, drinking three        glasses of wine with dinner, eating apple pie, etc.

(2) Device Decisions

-   -   (a) Time and amount of medications. These might include insulin        complements, enhancing insulin production, reducing insulin        resistance, slowing absorption of carbohydrates.    -   (b) Time and amount of each food/drink group—e.g., consumption        of carbohydrates.    -   (c) Time and amount of each activity.

(3) Outputs (Consequences or Results of inputs)

-   -   (a) Outputs related to diabetes such as amount of sugar in the        blood, lipids;    -   (b) Performance Index; e.g. how close to target values the blood        sugar is, plus the “burden” or side effects of the medicaments        taken.

The PDMS serves the patient as frequently as desired so that, givenConditions, it advises the best remaining decisions and predicts thecorresponding expected outputs. Alternatively, the user may suggest acomplete set of input values and the PDMS would display expected outputs(What-If function).

Some benefits for the user are: awareness of results and lowerprobability and gravity of hyperglycemia and hypoglycemia, and ingeneral, more flexibility and higher quality of life, what is called“wellbeing”.

The PDMS may be embodied as software to be used in a personal computer.In other embodiments, it may reside in a hand-held device such as aPersonal Digital Assistant (PDA) or be in a specially designed device.In still other embodiments, there will also be sensors such as glucosemeters to collect data automatically, and even automatic dispensers suchas an ambulatory insulin pump. Communications with sensor or deliverydevices in or on the surface of the body may be wireless. Equivalentlyfor other deceases, and for other activities and systems, includingskippering a sail boat, guiding a robot, or the operations of afuel-injection system.

ISPM Setup. Setup comprises defining the inputs, outputs; and settingthe objectives as defined by Constraints and the Performance Index—allof which depend on the system or situation to be managed. Setup may befixed during device production, or part or all be flexible to bere-defined by the user. In the case of PDMS, many variables may bemandated by a physician.

ISPM Operations. After the ISPM is seeded with sufficient individualsystem input/output data to be useful (explained below) its regular usewill include two activities:

(1) Data Entry: The user will enter, at some frequency to be determined,the new conditions experienced, the new decisions made (eitherindependently or with ISPM's advice), and results (outputs) experienced.In more advanced options some or all data may be collectedautomatically.

(2) Advice: When desired, given the Conditions, the user will questionthe ISPM for advice on what to do with the remaining decisions(including all) to maximize performance. The user may or may not followthe advice, but whatever he/she does, that will be actual data to beentered. In more advanced designs there may be options where some or alladvice may be implemented automatically. The ISPM would also display theexpected outputs from following the Advice, and the level ofuncertainty. It may also advise whether no action may producesatisfactory results (e.g., sufficient well-being); which may beinterpreted as that the Decisions in the Conditions are not sufficientlywise given the objectives to be achieved.

Seeding involves Data Entry of input/outputs for the individual system,for a period of time to be determined, for instance, one month. Afterthat period, the advice will be judged according to experience andcommon sense (e.g., medical criteria for PDMS) until it is deemedsufficiently reliable.

TECHNOLOGY. The basic assumption made is that each individual system hasa stable input/output system for a particular mode of operations. Thatis, the ISPM is applicable when the I/O system has a fairly constant“transfer function” that creates outputs as a function of the inputs. Ifthe transfer function changes, something else is the matter, andprobably an expert needs to look at what is going on. Ignoring animportant input that keeps on changing would appear as a transferfunction that changes. Thus, this device would work best when allimportant inputs are known and their values included in the setup. Forsystems that have “memory”, the outputs may also be a function of pastconditions and decisions.

A key concept in the Claim is that a sufficiently accurate transferfunction based on prior knowledge and data exists, and/or that it“learns” an approximate one by fitting a mathematical function to thedata. To do the fitting, one uses some of a variety of standard orespecially developed mathematical/statistical functions and/or computerprograms. Then in addition, standard or especially developedoptimization search engines to identify near-optimal conditionaldecisions as estimated by those approximate functions based on theconditions are needed. Examples are (in approximate order of age): (1)Linear Regression and Linear Programming (Optimization); (2) Non-linearRegression and Non-linear Programming (Optimization); (3) SequentialEmpirical Optimization (SEO); (4) Neural Networks (NN) and optimization;and (5) others more specialized for this application. Any of thesetechnologies may be customized and expanded so as to: (1) Update thefitted models periodically with new stored data, to refine its knowledgeand keep up with changes in the systems behavior. (2) Add built-incontrol rules so that, depending on conditions, some preliminarydecisions are recommended based on prior general knowledge for a familyof individual systems. The Device Decisions include modifications of theadvice by the preliminary control rules to best suit the individualsystem. In the case of PDMS, the “control rules” would be the current,static treatment recommendations. The control rules may be updated withrefined new knowledge and (3) Add a generic transfer function based onprior general knowledge for a family of individual systems, so that anadvice without any data would be a generic advice, not individualized.Then the fitting mathematics may create a correction transfer functionwhich, together with the generic transfer function, will characterizethe individual system's behavior. The generic transfer function may beupdated with refined new knowledge.

Some features of the ISPM solution: (1) learns how the I/O individualsystem behaves based on the history of data, even as the transferfunction changes slowly with time, such a due to seasons, wear-and-tear,age, etc. (2) may detect whether there is a sudden change in behaviordue to unknown causes, to trigger an alert to pay more attention or tobring to bear expert advice and possibly take corrective action outsidethe ISPM. (3) may detect whether past data seems to be incongruous orerroneous. (4) may detect the relative importance of the inputs as basedon the data. (5) may be download to a computer for analysis by expertsand management of past data and the setup.

The diagram of FIG. 1 is labeled as for the PDMS application. The dottedlines indicate flow of data. The green dotted lines indicates frequentexchange of data. The orange dotted lines are less frequent interactionsby which the user or experts guide the ISPM, or in this case the PDMS.

The “Data Base” and the “Functions” (including the transfer functionsand/or the built-in control rules) are really within the Device. Theexpert (e.g., a specialized physician in a PDMS) will review past dataand the characteristics of the (approximate) transfer function to drawinsights about the particular aspects of this input/output system (thepatient) and the conditions under which it is subjected. This analysismay result in resetting the specifics of the application (variables,objectives, rules, etc.).

A desirable manifestation of the optimization technology is SequentialEmpirical Optimization (SEO) already used as computer software foroptimizing production/manufacturing operating performance, but notincluding all the features described in this patent. SEO exists today inonly one commercial product ULTRAMAX®. The SEO Technology—Introduction.Basically SEO manages a sequence of periodic readjustment to the controlinputs of an input/output system with the aim to maximize the cumulativesequence of overall value delivered by the process. SEO continuallyupdates its learning from the stored run data. In this discussion, theterm “adjusting” control inputs is used the same as “setting” controlinputs, including adjusting or setting things by hand. (SEO might not,but the idea of this Device may include this logic).

Some earlier approaches to solve this problem were EVOP (e.g., Box andDraper, 1969) and SIMPLEX (Spendley, Hext and Himsworth; 1962, Walters,F. H. et al. 1991). As computer power became accessible to production inthe late 60's, Dr. Moreno lead efforts within Procter & Gamble todevelop and implement new algorithms taking advantage of what ispossible with computer programming and advanced statistical modeling inorder to vastly overcome the limitations of those earlier methods.

The SEO started in 1982 is the third version started from scratch.Several hundreds of individual processes have been optimized withULTRAMAX.

Desirable SEO features: These are the properties which are necessary, orhighly desirable, for managing process adjustments, and define SEO (fromMoreno, 1993, 1994): (1) User Control: As one aspect of flexibility,users are not required to follow the sequential advice provided by thetechnology. This is desirable for early acceptance. It is also desirableduring the earlier adjustment cycles when very little data has beencollected, to enable existing experience to be included in the run database.

(2) Sequential cycles may be stopped at any time to allow you tocontinue taking advantage of the gains achieved so far. Later on, youmay continue where you left off, making use of the stored run data.

(3) Multiple inputs, multiple outputs: It deals with multiple inputs(some adjusted, some uncontrolled) affecting multiple outputs (somedirectly measured or estimated, some calculated as a function of otherinputs and outputs with internal equations written by the user). Controlinputs include physical variables adjusted manually, setpoints, andcontrol logic parameters (such as gains for first-level controllers).Some important families of calculations are: (1) mass and energy balanceequations to estimate certain results which maynot be measured directly;(2) estimations of final results based on intermediate and inputvariables (e.g., NOx based on more readily measurable operatingvariables); (3) calculations of consistency vs. constraints orspecifications, such as CPK and Loss Functions; and (4) economicequations taking all costs (and potential revenues) into account.

Multiple objectives: The evaluation of process performance is determinedin quantitative terms defined by the team, where objectives are set bymanagement. Individual objectives might relate to measures such asyields, production rates, economic evaluations (costs and revenues),quality, safety and equipment life, losses, emissions, byproducts, andsatisfaction of regulatory constraints. Basically, if performance may bemeasured or estimated with sufficient accuracy and on a timely basis tolearn from it, it may be taken into account. Goals are represented by:(a) a “Performance Index” or “objective function”, an output to bemaximized or minimized (which may be a directly measured value, or afunction of other variables; (b) by necessary constraints on inputs andoutputs.

Processes with noise: It deals effectively with process outputs thathave “noise”. Noise is the level of output data variations when inputsremain constant. Like all empirical solutions, the larger the noise theless effective it will be.

May use prior valid data: The analysis may be primed with valid datafrom previous runs.

Prior Model free: It does not require that the user providemathematical/computer models of the behavior of the process. However, ifthe models are available it may use them to give the technology ajump-start. These models may just be approximations for some outputs asa function of some inputs.

Feed-forward adjustments: It may determine optimal adjustments for knownvalues of uncontrolled inputs (e.g., resource allocation, materialscharacteristics, ambient conditions, process conditions). This isfeed-forward optimization, based on predictive models (without requiringthe process to deviate from objectives before corrective action takesplace, such as in feedback control).

Feed-back adjustments: It may do feed-back optimization when the processchanges slowly due to changes in unknown uncontrolled inputs.

Closed-loop implementation: It is applicable in the spectrum fromStand-Alone (no integration), to integrated but full hands-on humancontrol (an Advisory capacity), to fully automatic on-line closed-loopoptimization without requiring human intervention and interpretation(where ULTRAMAX provides reliable direct supervisory control, includingefficient alerts).

Mathematical Formulation of SEO—The Process, Requirements and Optimum.The process behaves as follows (X, U, Y and are vectors):Y=F(X,U)+ε

where

X is the vector of adjusted inputs (control inputs), the decision thatcontrols operational performance;

U is the vector of uncontrolled inputs, with values determined elsewhere(by people or nature);

Y the vector of outputs (after transients due to readjustments), theoutcome or consequence from the inputs X,U.

Y0 is the value of the Performance Index or Objective Function;

F(X,U) is the steady-state mean (i.e., after transients due toreadjustments) process output vector, or the “response”. As we shallsee, the form of F is unknown, the same as for ε below.

F, as representative of production processes, is relatively “smooth” inthe area of interest. F may possibly be changing slowly with time (orequivalently, changing because of slow changes in unknown uncontrolledinputs not included in U).

ε is an bell-shaped noise vector with mean zero and covariance matrix Σwith the diagonal of variances, whose square root is the standarddeviation, sigma or noise vector N. It is most likely affected by X,U.With the understanding, contrary to remarks in vogue in the last fewyears in certain fields, that very few processes produce any datafollowing a Normal distribution. Note that there are almost no physicalprinciples that indicate that process outputs should be normallydistributed (except, e.g., the energy emitted by a black object vs. thelog of the frequency; the addition of several distributions of similarindependent standard deviations—Central Limit Theorem). On the contrary,for instance, a process often places limits—resulting in trimmingtails—which destroys a normal distribution. This has been confirmed byexperience, where with sufficient data one almost always may prove thatprocess output distributions are not normal.

ε is a property of the process AND of the OP, in particular, the inputsincluded and how Y is measured (e.g., averages of more raw data may havelower noise, especially if U does not change much). This concept ofnoise is smaller than in most other quality control analyses becausechanges in the known uncontrolled inputs do not contribute to noise,while they do in regular methods. Note that in this model “errors” areassigned only to the outputs. The inputs are presumed to be absolutelycorrect.

The requirements and optimal operations are defined as: Maximize theobjective function (Performance Index) by selecting the adjustments Xthat satisfies the constraint requirements, given the value of U. Theconstraint requirements are:

(a) The mean process values almost never violates the upper (UC) andlower (LC) constraints. In this discussion, “mean” is used to expressthe average for the same values of the inputs; “average” is used for theaverage across time (with possible changes in inputs).

(b) The actual data for each run almost never violates a constraint bymore than an amount “Minimum Important Difference” (MID). The MID allowsfor a “gray” area for the actual data constraint violation.

Mathematically: MaxX/U {F0(X,U)}—0 for objective function s.t. (wherein“s.t.” means “subject to”, or, within these constraint limits as ahigher priority); that is, while satisfying these the constraintrequirements:

Requirement (a):

X_(i)≦UC_(i)=inputs with upper constraint UC;

X_(i)≧LC_(i)=inputs with lower constraint LC;

F_(i)(X,U)≦UC_(i)=outputs with upper constraint UC;

F_(i)(X,U)≧LC_(i)=outputs with lower constraint LC.

Requirement (b):

Y_(i)=F_(i)(X,U)+ε_(i)≦UC_(i)+MID_(i) (most of the time);

Y_(i)=F_(i)(X,U)+ε_(i)≧LC_(i)−MID_(i) (most of the time).

The “most of the time” (b) requirement is translated into requiring thatthe practical worst noise level ε_(i) to be satisfied be 3-sigmas(3*N_(i)), called the 3-sigma Protection (although we are not using thecriterion of the probability of violating constraints in order to avoidhaving to make assumptions about the distribution of the data):

F_(i)(X,U)≦UC_(i)−3*N_(i)+MID_(i)

F_(i)(X,U)≧LC_(i)+3*N_(i)−MID_(i)

Similarly, practical optimization is when the achieved F₀ is within MID₀of the optimal one defined above. See FIG. 2.

So, bringing both constraint requirements (a) and (b) into one compositeset of equations, the optimum we are searching is defined as:

Given the values of U,

Max_(X/U) {F₀(X,U)}—0 for objective function;

s.t. X_(i)≦UC_(i)—inputs with upper constraint UC;

X_(i)≧LC_(i)—inputs with lower constraint LC;

F_(i)(X,U)≦UC_(i)−max{3*N_(i)−MID_(i), 0}—outputs with upper constraintUC;

Fi(X,U)≧LC_(i)+max{3*N_(i)−MID_(i), 0}—outputs with lower constraint LC

which defines the optimum adjustment X* and the mean optimum outputsY*=F(X*,U); both for each value of U.

The practical objective is not quite to find the optimal X, but todefine Xs that satisfy all constraints and produces mean outputs nofurther away from the optimal F(X*,U) than MID0. The set of such Xsdefines the Window of Operations.

If there are upper and lower constraints and the Safety Buffer (definedbelow) is sufficiently large, there is no Operating Range and theprocess is totally incapable. In this case optimization makes littlesense—the problem should be fixed.

An equivalent formulation of the output constraints is:

UC_(i)−F_(i)(X,U)≧max{3*N_(i)−MID_(i), 0}—outputs with upper constraintUC;

F_(i)(X,U)−LC_(i)≧max{3*N_(i)−MID_(i), 0}—outputs with lower constraintLC.

This form has a useful interpretation. The left-hand-terms are the meanSlacks (how far from constraints, negative is beyond); theright-hand-term is defined as the Safety Buffer; as follows:

Mean Slack_(i)=UC−F_(i)(X,U)—(upper);

Mean Slacks=F_(i)(X,U)−LC—(lower);

Safety Buffer_(i)=max{3*N_(i)−MID_(i), 0} (recall, Protection=3*N_(i));

Thus, the optimum output constraint requirements are simply that:

Mean Slack≧Safety Buffer

Note that for the active outputs (that is, with active constraints) ifthe noise is

${N_{i} < \frac{{MID}_{i}}{3}},$sufficiently small, then the Safety Buffer is zero, and the onlyconstraint requirements is for the mean process values [requirement(a)]; and reduction of noise does not help except for the PerformanceIndex. If the (mean output) constraints and the MIDs are properlydefined from a business point of view, this is the sign of a processunder good process control (and quality data).

On the other hand, if MID is zero, the only constraint requirement isfor the actual process actual values [requirement (b)]. In this case,any reduction of noise for an active output results in potentialimprovements of the Objective Function (Performance Index).

Sequential Empirical Optimization (SEO). The characteristics of aSequential Empirical Optimization solution to optimize a process are:

(1) It is Empirical: that is, series of run data {X,U,Y} are known, butsome (or all) F(X,U) and ε are not. In particular the structure of F isalso not known except to assume reasonable “smoothness”.

(2) It is Sequential: that is, it continually stores operating (run)data and continually uses the stored run data and extracts from itinformation and knowledge to create updated sequential advice to adjustthe process next. Then a process run produces a new {X,U,Y} which inturn is stored in the database and repeat the cycle. Thus SEO creates aseries of {X,U,Y}t stored in a data base. The first Xt is theadjustments at Baseline, defined by the user.

Note how the sequential analysis above emulates the process of a mindgaining experience through repeated action, and using the rememberedactions, conditions and outcomes to make better action decisions in thefuture, and aspect or learning and skill development.

Sequential analysis has a very valuable advantage (à la [3] Wald): ituses the information in each sequential run immediately in order torefine knowledge and to decide where to run next. By comparison, inother more parallel empirical studies (DOE, Neural Networks) the valueof the information is not used until the data is analyzed at the end.This is the most important reason why a properly implemented SEOtechnology is the fastest empirical optimizer available today. The othersynergistic reason (bootstrapping) is that since SEO approaches theoptimum faster, the database has relatively more data around theoptimum, which is the most valuable data to understand the location ofthe optimum (recall, SEO does not assume knowing the structure of theprocess transfer function).

SEO Maximization is meant as a local optimum at the end-path ofcontinuous improvements from the starting adjustments, and ideallyjumping over minor local optima. In particular, finding “othermountains” to climb is taken as a responsibility of engineering or R&D,not of daily production.

The performance of a SEO solution—for the likely process forms ofY=F(X,U)+ε that will be optimized—is evaluated, basically, by:

How quickly the series {X,U,Y}t converges to the optimum {X*,U,Y*}.

How closely the series {X,U,Y}t approach to the optimum {X*,U,Y*}.

In manufacturing, which is a continual value generation situation, theperformance of SEO is evaluated by the cumulative process performancewhile following the advice provided by the technology (The concept isvery clear when there are no constraint violations: find the cumulativeor average Performance Index. When there are violations, this approachrequires placing a penalty for constraint violation, which is somethingsomewhat arbitrary.)—or more specifically, since the implementation ofoptimization is started. (This clarification is made to take intoaccount the performance losses and time that it takes to collectexperimental data for model-based alternative approaches (such as DOEand NN); and the time to design and implement the first-principle modelsand collect data to validate it.)

In engineering and development the performance may be evaluated by thecosts and delay incurred in the number of “experiments” and by how closethe solution approaches the optimum.

Depending on the SEO technology used (or on how it is adjusted),achievements in terms of the objective function F0 may frequently beincreased at the sacrifice of increasing the incidence of violating someconstraints, and thus the SEO needs to proceed carefully. The sequenceof Xt needs to be very “intelligent”: low risk and effective. It musthave a correct balance between:

(1) being conservative so as to avoid making the process violateconstraints or sacrifice performance too much,

(2) being bold, moving towards improved performance,

(3) creating new data so that future models have improved predictioncapabilities,

(4) providing compensation for known values of the uncontrolled inputs(feed-forward optimization), and

(5) reacting to changing behavior due to unknown uncontrolled inputs(feedback optimization).

The ULTRAMAX solution for SEO-Fundamentals. In addition to storing rundata, the solution includes of two basic functions: Learning (creationof predictive models); and Synthesis (using the models to generateadvice for the next run).

Learning, model building. Models of generic form M(X,U)≅F(X,U) arecreated for new values of U or when new run data is collected in thedatabase, based on the set of historical run data {X,U,Y}t.

At the same time the technology creates:

(1) An Area of Confidence (AOC), which is the region in X,U where M(X,U)is most accurate—within the region covered by {X,U}t.

It is calculated by an elaborate pattern application of the Mahalanobis[1936] distance which allows the AOC to be concave or composed ofdisjointed areas.

(2) An estimate Σ of ε of and the noise n; The prediction models enable“What-if” estimation or outputs given the inputs (forward analysis).

Synthesis, Advice. Synthesis is almost the reverse of What-if. Synthesisis finding the values of the inputs which satisfy some criteria for theestimated outputs. Here it is “which-is-best” for decision-making. Thesolution is the same as for the theoretical definitions of optimum,except that:

(1) It is obtained with estimated functions M and noise N. Inparticular, the prediction error E_(i) depends of the level ofextrapolation, as typical of regression models.

(2) {X,U} belonging to the AOC, where M is most accurate.

(3) Adds certain perturbations to that future models be morerepresentative but without sacrificing performance much. (This is theleftover of the benefits of orthogonality in DOE.)

The Advice X for the next run is produced by:

MaxX/U {M0(X,U)}—0 for objective function;

s.t. X_(i)≦UC_(i)—inputs with upper constraint UC;

X_(i)≧LC_(i)—inputs with lower constraint LC;

F_(i)(X,U)≦UC_(i)−max{3*E_(i)−MIDi, 0}—outputs with upper constraint UC;

F_(i)(X,U)≧LC_(i)+max{3*E_(i)−MIDi, 0}—outputs with lower constraint LC;

{X,U}εAOC—Area of Confidence plus adding periodic perturbationsIncluding the constraint of being in the Area of Confidence, in additionof assuring acceptable prediction errors, enables dealing with inputdata which follows certain patterns, such as near-collinearities. Note:For people accustomed to desiring orthogonal data, let us clarify that aproperly formulated Optimization Plan will have independent controlinputs, thus any collinearities are not the result of cause-and-effectrelationships between the inputs. Note further that in moving theadjustments from the current region to the optimal ones, it is desirableto move along ridges so as achieve maximum cumulative performance. Thisleads to having near collinear inputs, but only due to an intelligentoptimizer getting desired outcomes.

Note these two properties about M for SEO which yield significantsimplification and effectiveness in calculations: (1) It is not requiredto understand the effects of each input separately; it is just necessaryto be able to predict results in the AOC region. Thus, with theprotection afforded by the AOC, confounding of input effects is oflittle consequence. (2) Trying to make M accurate away from the optimum(e.g., by making its generic form too involved or by fitting data inthat region) is not only is irrelevant but it distorts the fitted modelsresulting in lesser accuracy around the optimum.

These properties are less demanding on M(X,U) than the usualrequirements for DOE, Neural Networks and First Principle models (eachfor different reasons).

Note this characteristic of SEO: if a sequential advice is relativelypoor because of making an incorrect inference, the run is likely toprovide data that reveals the issue: (good) sequential empiricaldecision analyses are self-healing.

Details—Learning (update models). The ULTRAMAX solution createsprediction models M in three ways. The solutions 1 and 2 are amultivariable quadratic or second-order Taylor approximation of theresponse surface F for each output as a function of all inputs.

(1) Bayesian Statistics. Models are crated with Bayesian statistics whenthere is little data. This compares with the computationally much fasterClassical or Fisherian statistical models least-sum-square fits. Also,in general the Bayesian framework is superior for decision makinganalysis—as compared to scientific true-false assessments best made withclassical statistics. Bayesian solutions enable us to utilize theinformation available in as little as two runs, with different inputvalues, to already be able to move sequentially in a direction of likelyimprovements.

This thought problem illustrates what is desirable. Imagine: (1) Aprocess with two adjustments and one result to be maximized; (2) The twoadjustments are represented by two sides of a room, and the results byheight; (3) Two process runs (with different adjustments and differentresults), which correspond to two points in space in the room (whileexplaining this, hold the point at the tips of the thumb and indexfinger of each hand).

Now, ask the question: in which direction should we change theadjustments next with the highest likelihood of improving results?Obviously we would move the adjustments from the low performing run toand passing the high performing run. (1) The simplest prediction modelstructure that would recognize the effects of the adjustment inputs is alinear model with three parameters (a constant and a coefficient foreach input). (2) Classical statistics may not create a prediction modelwith the above data (there are two data points to estimate threeparameters). (3) Bayesian statistics may create such a model; and theresult matches what our intuition indicates: move adjustments from thelower height run towards the higher height run, going beyond the higherrun adjustments. (The MID indicates how much further we may go.) (4) Theabove holds true for any number of inputs! Thus, if we have 20adjustments, ULTRAMAX's sequential Bayesian models will likely beobtaining performance improvements starting from the third run, whileclassical statistics maynot even venture a guess until the 22nd run.Neural Networks, having many more coefficients, would be much worse inthis respect.

The application of Bayesian regression starts with linear models withvery limited data, and then moves on with the full quadratic with moredata. The solution is explained by Moreno (2006) and Hurwitz (1993):

-   Adelman, A., and W. F. Stevens (1972). “Process Improvement by the    ‘Complex’ Method,” AIChE Journal, Vol. 18, No. 1, p. 20.-   Bhateja, C. P. and C. W. Moreno, (1989) “Practical Optimization of    Production Grinding Systems”, SME Modem Grinding Technology Clinic,    Oct. 10, 12, 1989; Detroit, Mich.-   Box, M. J. (1965). “A New Method of Constrained Optimization and a    Comparison With Other Methods,” Computer Journal, Vol. 6, p. 42.-   Box, G. E. P., and N. R. Draper (1987). Empirical Model Building and    Response Surfaces, John Wiley & Sons, New York, N.Y.-   Box, G. E. P., and N. R. Draper (1969). Evolutionary Operations,    John Wiley & Sons, New York, N.Y.-   Colosimo, B. M., del Castillo, E. (2006). “Bayesian Process    Monitoring, Control and Optimization” Chapman & Hall/CRC. ©2007;    Boca Raton, Fla., USA. December 2006. See C. W. Moreno (2006) below.-   Draper, N., and H. Smith (1966). Applied Regression Analysis, John    Wiley & Sons, New York, N.Y.-   Dharmarajan, N. N., and Patterson, P. D. “Boiler Tuning with SPO:    Critical First Step in NOx Compliance Strategy of Central & South    West Corporation.” Paper presented at AWMA Conference and Expo,    Nashville, Tenn., June 1996.-   McVay, M. and Patterson, P. D. (1998) “Illinois Power's On-Line    Dynamic Optimization of Cyclone Boilers for Efficiency and Emissions    Improvement”, Int'l Joint Power Generation Conference, Baltimore,    Md., Aug. 24, 1998.-   Moreno, C. W. (1993) “How Modem ‘Smarter Not Harder’ Technologies    may Simultaneously Maximize the Combination of Pollution Reduction    and Business Success”, Conference on Environmental Commerce,    CONEC'93, Chattanooga, Tenn., Oct. 17-20, 1993.-   Moreno, C. W. (1994) “Gaining Control of Plastic Forming Machines    with new Technology for On-line Adjustments”, Structural Plastics    Conference, Apr. 10-13, 1994, Washington, D.C.-   Moreno, C. W. (1995-2006) The Blue Book: Maximizing Profits through    Production, Ultramax Corporation, Cincinnati, Ohio, USA.-   Moreno, C. W. (1999) “Improvements Through Process Adjustments”,    Amerimay Statistical Association Quality and Productivity Research    Conference, May 19-21 1999, Schenectady, N.Y., USA.-   Moreno, C. W. (2001) “Comparison of two well known Methods for    Optimizing Power Plant Operations”, 44th Annual ISA Power Industry    Div. Conference, Orlando, Fla., USA Jul. 7-13, 2001-   Moreno, C. W. (2006) “Software: A gentler step-up”, InTech, ISA    (Instrumentation, Systems and Automation) Society, April 2006,    pp. 45.    www.isa.org/InTechTemplate.cfm?Section—Article_Index1&template=/ContentManagement/ContentDisplay.cfm&ContentID=53257-   Moreno, C. W. (2006) “An Application of Bayesian Statistics to    Sequential Empirical Optimization” Chap. 11, page 291, of “Bayesian    Process Monitoring, Control and Optimization” by Colosimo and del    Castillo—see above.-   Moreno, C. W. and Yunker, S. B. (1993), “Reducing NOx Emissions and    Improving Boiler Efficiency Using Synthetic Intelligence”,    Conference on Expert System Applications for the Electric Power    Industry, Phoenix, Ariz., Dec. 8-10, 1993.-   Nachtsheim, C. J. (1987). “Tools for Computer-Aided Design of    Experiments”, Journal of Quality Technology, Vol. 19, No. 3, July    1987, pp. 132-160.-   Sachs, E. M., R. Guo, S. Ha, and A. Hu, (1991), “Process Control    System for VLS₁ Fabrication”, IEEE Transactions on Semiconductor    Manufacturing, Vol. 4, No. 2, May 1991 Abstract-   Spendley, W., G. R. Hext, F. R. Himsworth (1962). “Sequential    Applications of SIMPLEX Designs in Optimization and EVOP,”    Technometrics, Vol. 4, pp. 441-61.-   Wald, A. (1947). Sequential Analysis, John Wiley & Sons, New York,    N.Y.-   Walters, F. H. et al. (1991). Sequential SIMPLEX Optimization, CRS    Press, Inc., Boca Raton, Fla.

True Bayesian analysis, involving multidimensional integrals, are verytime consuming—as compared to classical regression analysis.Quasi-Bayesian regression models are created with this computationalshort-cut: use dummy “prior data” with “null” knowledge and classicalregression analysis. The result is very similar to ridge regression,which was illustrated by Hurwitz to be equivalent to the Bayesiananalysis.

When there is sufficient data to calculate all coefficients withClassical statistics and have enough extra to calculate the noise withclassical regression, the models are said to be “complete”.

Full quadratic models have

$\frac{\left( {N + 1} \right)\left( {N + 2} \right)}{2}$coefficients, where N is the number of inputs. The minimum number of rundata required to get complete models is this value plus the desirednumber of degrees of freedom, approximately 2*N+. Then PRIOR=0 in theModel and the Coefficients reports, and models are “complete”. Then alsomost indicators of model suitability such as the “noise” and “signal”became more reliable.

Goal-oriented, Locally Accurate Models. A breakthrough awareness andprinciple is to realize that: For optimizing, we only need to predictwell around the optimum (or best predicted running conditions). (Thereare a few more requirements. The important thing is to recognize thatpredicting equally well everywhere is not very relevant foroptimization, ans is the burden of virtually all empirical solutions.)

(1) When more data than necessary for complete models becomes available,weighted linear regression is used to focus on optimal operations, toinclude only data which produces the most accurate models around wherethe best estimated results. (Obviously, this is not regular regression.)Note that for doing optimization, representing the response surface awayfrom the optimum is actually a detriment since this tends to distort thefit of the models around the optimum (unless the model structure is veryrepresentative).

(2) Locally accurate quadratic models largely eliminate the concern oflack-of-model fit for “smooth” response surfaces.

(3) The models also weight older data less when it detects deviations inprocess behavior from past patterns. (This happens when the process isdeigned “dynamic”.)

(4) The input spread of the series {X,U,Y}_(t) depends on the noise andthe MID.

Calculated outputs. Calculated outputs are a function of inputs and/orof other predicted outputs. These models have the followingcharacteristics:

(1) they provide guidance as to which direction to follow forimprovements even with limited data.

(2) they adapt well to slow dynamic changes in process behavior due tounknown causes;

(3) they tend not to be badly fooled into fitting noise rather than theunderlying process behavior (due to the Bayesian analysis with littledata, and due to heuristics to select data when there is excess data togenerate locally accurate models).

(4) since they have relatively few coefficients (e.g., as compared withNeural Networks), they are capable to extrapolate further going from thestarting set of adjustments to the optimal ones.

(5) deal effectively with smooth response surfaces with structuralnon-linearities and smooth non-constant interactions among the inputs(e.g., curved ridges).

Synthesis (generate advice). The SEO resolution of the mathematicalformulation above is done with a non-linear programming algorithm whichmay:

(a) solve conditional optimization;

(b) has reasonable abilities with mixed-integer solutions (but not whenapproaching few categorical values such as zero-one programming).

Synthesis is created by this prioritization:

1^(st) inputs obey their constraints.

2^(nd): when giving Advice only (not for the Optimum Estimate), giveAdvice where each input does not change by more than its MID. (There isalso a multivariable limit on the “travel” of the re-adjustments:Travel≦PAR(41).)

3^(rd): {X,U}εAOC, to assure relative certainty of estimates;

4^(th): Multivariable limit on the “travel” of the re-adjustments:Travel (A Mahanobis distance.)≦PAR(41);

5^(th): outputs obey their constraints;

6^(th): optimize the Performance Index or objective function (often plusa perturbation function).

Synthesis may be seen as the coding of “volition”, or a goal orientedattitude in the part of the technology, to attempt desired betterresults not experienced before. A basic reason this is possible isbecause of the implicit assumption of the reasonableness ofinterpolation and extrapolation in systems with gradual variables. Withthe current technology this is much more difficult to achieve withcategorical (extremely discrete) decision problems. Other specificsabout the technology used were published in Moreno & Yunker 1992, 1993,Moreno 1986, 1988, 2006, as listed above.

Reference to Artificial Intelligence. All together, Learning andSynthesis, like most algorithms, are a series of heuristics or ruleswhich are based on the accumulated experience of experts and thecreators of the technology. As such, the technology may be seen as an“Expert System” to learn how to adjust a process to optimize itsperformance.

I have previously noted the distinction that ULTRAMAX has a domain of arelatively small number of variables with infinite values, whiletraditional Expert Systems tend to deal with very many elements, each ofwhich is characterized by a few discrete (categorical) values. Further,it suggests that “Artificial Intelligence” should have thecharacteristics of Cognition and Volition, where Cognition is defined as“Learning about how a system is and may be”, and Volition as “Creatingor selecting options based on an evaluation of consequences”. Reflexbehavior, such as a standard PID control unit for productionenvironments, has neither cognition nor volition, and therefore it isnot Artificial Intelligence.

Some further characteristics:

Types of variables: It is restricted to using control inputs and outputswhich behave gradually, in order to be able to use the modeling methodsdescribed above. “Gradual” means that the variables are potentiallycontinuous, but may be discretized in small steps. It does not handlecategorical factors except for the component inputs.

Time frame: ULTRAMAX runs “real-time” with a time frame for processadjustments in the order of every several minutes to hours, or batchtime. While software CPU is a consideration for short cycles, the mostfrequent determining factors of the time between adjustments (Run Time)are: (a) in continuous processes: how long it takes to get tosteady-state due to readjustments of the control inputs, so that theoutput data truly reflect the current inputs, rather than effects fromprevious ones (unless using the “Transient Version” of SEO); (b) inbatch processes: the length of a batch processing; (c) during the firstcycles while converging the optimum, how long it takes after thecompletion of a run to measure the values of Y—e.g. from a laboratoryexample. (d) how frequently U changes. By comparison, the time frame for(first-level) “process control” is in the order of a second orfractions.

In process control this is what most people understand as “real-time”.(a) Quick adaptation to alternate goals: The evaluation of processperformance may be made through calculated variables (most frequentlythe Performance Index is calculated), which may be a function of GlobalFactors. The Global Factors represent targets, tolerances, unit costs,etc., and they may easily be changed from a central location in thesoftware. In particular, calculated variables are recalculated with newfactors to portray old (valid) physical data with today's objectives andevaluation factors, and the sequence of advice will move quickly to thenew optimal conditions. This also means that the Optimization Plan doesnot have to be worked through in total detail. In fact, it is common forthe awareness of true optimization objectives to evolve significantlyduring the course of SEO.

Noise: Changes in known uncontrolled inputs are not a source of “noise”,as in most other statistical approaches. For ULTRAMAX their effects areunderstood, and managed. While uncontrolled inputs are “special causes”for SPC, ULTRAMAX provides the means to cope with them. The unknownuncontrolled inputs, and changes in the known uncontrolled inputs withina run are noise factors.

Optimum Estimates: There are three “flavors” of Optimum Estimates madeby the software:

(1) The Potential or Projected Optimum Estimate (POE) (within the AOC),where the expected prediction errors are calculated on the presumptionthat there is plenty of data around it as if it were interpolation, aswhen there is actual data around this optimum.

(2) The Advice & Optimum Estimate (AOE) has sufficient protection to bean advice to actually run the process. Here the expected predictionerrors take into account the increase in uncertainty due toextrapolation into regions with less or no data. (For the AOE, whenextrapolating, the protection sometimes needs to be less than 3-sigma inorder to enable faster progress moving towards an optimum close tooutput constraints.) When AOE is close to POE, then ULTRAMAX hasconverged to the optimum—which happens when the SDF of Opt.Est.≦0.4regularly.

Advice: (1) The Travel-Limited Path (TLP) are intermediate advice movingfrom the current adjustments towards the AOE, but which maynot movedirectly to the AOE because of constraints on of maximum changes inadjustments. (Defined by the user through PAR(59) and the MID.) (2)Advice w/ Exploration, the product of the perturbation.

Performance Characteristics. Starting from the current operations,ULTRAMAX's Sequential Optimization provides higher cumulative processperformance—cumulative contribution to profits or any metric defined bythe end-user—than any other empirical technology today, and perhaps thanANY technology—The alternate to “empirical” is “first-principle based”models, which take a very long time to make and validate to the samedegree of accuracy—while the process continues working at the currentconditions. One case where first-principle models may do better is whenrequired measurements are not available on-line. This is equivalent totaking fewer runs (readjustments) to approach the optimum, and to getcloser to the optimum.

This property is valid when starting away from a (local) optimum (Ifstarting at an optimum, the best performance is obtained by doingnothing different, which is a strategy which does not allow gettingimprovements when starting away from the optimum.); and applies: (a)Almost definitely when there is only one local (and thus global) optimaloperating performance. (b) Almost always when the process is dynamic(slow changes in unknown uncontrolled inputs), as alternate technologiestoday tend to be much more static. (Still, ULTRAMAX may only handle slowchanges in unknown uncontrolled inputs.) (c) Most probably for a year orlonger, when there are multiple local optima and ULTRAMAX converges toan inferior local optimum.

ULTRAMAX may converge to a local optimum because it starts from currentoperating conditions and follows a maximum-gain path towards the top ofa “mountain of performance”, which may be a local optimum. (Considerablemore effective than gradient-ascent, especially when there areinteractions between the inputs.)

By comparison, Neural Networks (NN) [like Design of Experiments (DOE)]require up-front many experimental runs with the production processitself, aiming at covering a large area of potential running conditions.Note that during these experiments the cumulative performance sufferssignificantly (constraint violations, unit costs, throughput, quality,emissions, equipment life, etc.)—which constitutes a significant hiddencost of the implementation.

However, in the case of NN, the model may represent various “mountainsof performance” within the explored input area, and the attachedoptimization logic would be able to select the best such mountain. Thisbest mountain may be better than the one that ULTRAMAX converges to. Insuch case, in the course of time and as long as the process is notchanged or improved, the NN cumulative performance will eventuallyexceed that obtained with ULTRAMAX. If the process changes then the NNmodels will have to be recreated with a new set of experimental runs,incurring again the associated operating costs; while ULTRAMAX, with alag, automatically keeps up with it.

Note the contrast: SEO is both an interpolation and a sequentialextrapolation tool, while Neural Networks and Design of Experiments arebasically interpolation tools. Note also that no interpolationtechnology may assure identifying the global optimum, as it might existsoutside the area chosen up front for gathering the experimental data,and will remain so until the experimental area is expanded to where theglobal optimum is. On the other hand, if there is only one localoptimum, ULTRAMAX will converge towards it wherever it is; and willmaximize the cumulative performance. (More narrowly, ULTRAMAX willalways converge so some optimum, while NN and DOE only to one within thearea of data coverage.)

Technical Performance. The performance capability of ULTRAMAX is, ingeneral and simplified terms, as obtained through replicated experimentsoptimizing a variety of simulated processes:Regularly, ULTRAMAX's SEOaverage performance will get relatively quickly as close to the optimumas the order of magnitude of the noise level.

The losses vs. ideal Performance Index in the Effective Operating Area(in turn reduced by the noise in the outputs) increase with the numberof inputs of the application; and is relatively better the more activeoutputs are calculated. (To the extreme that if all outputs arecalculated then no data is necessary—the reality is fully represented bycalculations.) Further conditions are described below.

Recall that for ULTRAMAX, noise DOES NOT INCLUDE the effects of changesin uncontrolled inputs for which there is data from run to run, whilenoise does with many quality control solutions.

This assumes not running into a region of “insensitivity” (definedbelow).

As a practical consequence of the above:

Let us assume that at the starting operating conditions constraints areregularly satisfied, namely, gains are only on the Performance index orobjective function. To get improvement the noise of the objectivefunction N₀ needs to be small (like ⅓^(rd)) in comparison to thepotential improvements in going to the optimum. If the potentialimprovements are lower than the noise then we will only be able to provethat we are running at near-optimal conditions for the current noiselevel.

It is possible to get better optima by reducing the noise of theobjective function and active constraints, which is practical to dountil 3*N=3*sigmas≦MID. The condition

3*N=3*sigmas≦MID:

makes sense because such level of control on operations would regularlysatisfy management daily concerns from production about forecast andsafety (as represented by MID values management defined). (This has beenan underlying message in Quality Control, such as Dr. Deming, Dr. Juran,Six-Sigma, etc.) This is a lesser requirement than Six-Sigma, with theconsequence that where Six-Sigma is already implemented the data willhave even less noise and this will make it very easy to optimize well.

Note that the variables used, MIDs, objectives and constraints maychange with time as the business conditions and awareness of issueschange (easily done with an sequential analysis solution).

ULTRAMAX is a tool for active on-going process management, not to beplaced in the background to run-by-itself and be virtually ignored,otherwise the optimization plan will sooner or later become obsolete;and further, the end-user will miss the advantages detecting problems(out of statistical control condition), and will miss developing abetter understanding of the process.

Why better? ULTRAMAX's SEO is faster for two basic reasons:

(1) Most importantly, because of the Sequential Analysis; the fact thatthe information in new data is used right away to generate knowledge,rather than leave the information unused in model-centric solutionsuntil the data is analyzed at the end.

(2) Bayesian statistics, that enable moving into possible improvementsstarting with the third run.

ULTRAMAX's SEO is likely to get closer to the ideal optimum because ituses locally accurate models.

Other Specifics. The run averages deviate from the optimum F₀(X*,U) dueto:

(1) Imperfect knowledge because of the existing noise N in the data, andlack of model fit. In static processes these effects decrease as moredata becomes available. The amount of sequential data converging to theoptimum has two positive effects:

(1) increases precision by the traditional 1/√{square root over (n)}factor, and (2) enables the local model to be accurate in the smallerregion where the lack of model fit (or variations in the noise level) isnot important in comparison to the existing noise.

(2) Failing the (B) condition below if gradients get reduced too much inthe path converging to the optimum.

(3) The need to maintain a spread of the input data so that thecontinually updated empirical models maintain the knowledge about F(X,U)around the optimum, and also to pick up slow dynamic changes fromunknown causes. (The mechanism to pick up slow changes of unknown originis to treat the process as dynamic (PAR(49)=1), which enables the dataweighing to discount data because of its age. ULTRAMAX will graduallyrealize that using later data creates better fits, and this provides forgradual and slow feedback adjustment of the new modes of operations (notunlike the concept of running average). Conceptually, continuouslychanging unknown uncontrolled inputs may change at such frequencies thatULTRAMAX might enter a situation of resonance; but this has never beenseen.) The amount of spread may be managed.

The above performance characteristics apply when:

(A) All the ULTRAMAX Parameters are set at default, or with somerefinements suggested by Ultramax Corporation.

(B) Do not run into an insensitivity condition.

Common sense (and theory) indicates that convergence to an optimum willnot happen if the noise is too large in comparison to the gradient (Moreexplicitly, this is the “effective” gradient, the change in output forthe likely changes in inputs, not the infinitesimal gradient ofcalculus. It may be said that we are concerned with “differenceanalysis” rather than “differential analysis”, as it befits makingsequential adjustments with significant changes in values. Thus, inaddition to the requirement of the slope of a ridge, we are concernedwith the curvature of a ridge.) times the willingness to change theinputs. (The same reason why Screening Experiments sometimes fail.)

Limits to changes in the control inputs from one set of adjustments tothe next is determined by their MID and constraint ranges on inputs; andif small enough it will hung up Sequential Optimization. A metric thatreflects this situation is a small Signal—but amazingly, ULTRAMAX hasoptimized with small signals also.

Examples of outputs from the ISPM apparatus are depicted in FIGS. 3 and4.

It should be appreciated that any patent, publication, or otherdisclosure material, in whole or in part, that is said to beincorporated by reference herein is incorporated herein only to theextent that the incorporated material does not conflict with existingdefinitions, statements, or other disclosure material set forth in thisdisclosure. As such, and to the extent necessary, the disclosure asexplicitly set forth herein supersedes any conflicting materialincorporated herein by reference. Any material, or portion thereof, thatis said to be incorporated by reference herein, but which conflicts withexisting definitions, statements, or other disclosure material set forthherein will only be incorporated to the extent that no conflict arisesbetween that incorporated material and the existing disclosure material.

While the present invention has been illustrated by description ofseveral embodiments and while the illustrative embodiments have beendescribed in considerable detail, it is not the intention of theapplicant to restrict or in any way limit the scope of the appendedclaims to such detail. Additional advantages and modifications mayreadily appear to those skilled in the art.

1. An individual system performance management device said deviceadapted to provide adjustment advice for continual near-best operatingperformance of said system; said device incorporating algorithmicanalysis/synthesis within the device such that said device refines itslearning about said system's response as operating data is collected;said device further providing updated adjustment management advice; saiddevice being further adapted to detect abnormal behavior for expertanalysis and resolution and to provide dynamic awareness for when thereare changing conditions; and wherein the device is adapted to performboth forward analysis and backward analysis, where forward analysis isbased on an estimated input and backward analysis is based on anestimated output.
 2. The device of claim 1 where said algorithmicanalysis/synthesis is accomplished by Sequential Empirical Optimization.3. The device of claim 1 where said system is a management system for apatient with a chronic disease adapted to provide disease managementadvice under usual states for that disease, said advice being focused onwhat said patient should or could do and being tailored to the specificconditions and response of the patient.
 4. The device of claim 3 whereinsaid chronic disease is diabetes.
 5. The device of claim 1 wherein saidanalysis is accomplished locally.
 6. The device of claim 1 wherein saidanalysis is accomplished by a centrally-located server.
 7. The device ofclaim 3 wherein said device generates suggested treatment based uponindividual response.
 8. The device of claim 3 wherein patient data iscollected by one or more medical sensor devices.
 9. The device of claim3 wherein when said device detects abnormalities and directs saidpatient to obtain further advice and diagnosis by medical experts. 10.The device of claim 3 wherein the determination of treatment isdetermined by recording and analyzing said patient's response toconditions.
 11. The device of claim 3 wherein said device is dynamic inthat it is adapted to consider changes in individual response as afunction of time.